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Index investing has grown significantly over the past 30 years. Back in 1990, few were even aware of the option for indexing, and options were limited mostly to a handful of conventional mutual funds tracking the U.S. S&P 500 index. In 1993, Boston’s State Street Global Advisors launched the first S&P 500 index-tracking exchanged traded fund (ETF), with ticker SPY. Today this ETF controls over USD$300 billion in assets. Thousands of other index-tracking mutual funds and ETFs, tracking numerous different indices, in numerous different world markets and regions, are now in operation; in the U.S. alone, there were 1716

Many individual investors employ mutual funds as an alternative to direct ownership of stocks or bonds.

Indeed, mutual funds have some advantages:

Diversity: Even a single fund can encapsulate a large sector of the market. Peace of mind: One is less likely to stress out about sudden bad news regarding a particular firm if one owns shares in it only indirectly as part of a large mutual fund’s portfolio. Management fees: Several leading index mutual funds have even lower management fees than corresponding exchange-traded funds (ETFs). And as a class, mutual funds have significantly lower

The “January effect,” in common with the “Halloween indicator” and “sell in May and go away”, is a catchy, get-rich-quick investment idea adored by financial commentators because it is so easy to explain to unsophisticated readers. It rests on the claim that the U.S. stock market performs better in January, compared to the other months in the year.

Unfortunately, financial reports promoting the “January effect” are often vague and confusing. One recent example is here, which, like others in this genre, lacks a specific actionable investment strategy. In fact, this particular report does not even

“Big data” is already a frequently-heard buzzword, both in the business analytics arena, but also in the field of high-performance scientific computing. Basically, “big data” encompasses the collection, processing, indexing and utilization of large-scale datasets. Some concrete examples include temperature and sunlight data downloaded from satellites monitoring of the Earth’s environment, particle tracking data produced by the Large Hadron Collider in Europe, and anonymized smartphone position data made available, in some cases, by wireless operators and even certain smartphone applications.

Courtesy Quandl, DigitalGlobe and Orbital Insight

Big data has enormous potential to revolutionize the world of finance, mainly

David H. Bailey will join other speakers at the Artificial Intelligence and Data Science: Capital Markets conference, to be held 6-7 December 2017 at the National Museum of the American Indian (NMAI), One Bowling Green, New York City.

According to the conference website,

Complex mathematical modelling has always been part of the data-driven financial world, but today professional money managers are exploring a new range of techniques including machine learning, deep learning and neural networks. They have also become familiar with the relatively new discipline of data science – really an intersection of software engineering, statistical modelling, research analytics,

One of us (Marcos Lopez de Prado) has been interviewed on the topic of educational training in the finance field by Institutional Investor. A brief synopsis of this interview is below. The full article is HERE.

Many now accept that artificial intelligence, robotics and other high-tech developments will upend blue-collar professions such as retail sales, truck driving, package delivery, fast food and more. Some observers now estimate that self-driving vehicles could replace 1.7 million truckers in the next decade. Drivers of delivery vehicles could see their jobs replaced by Amazon drones.

But what about finance, the epitome of white collar employment? Far from being immune, white collar occupations in general, and finance in particular, are arguably even more prone to be substantially affected. Entire categories of highly-paid workers could be rendered obsolete in a matter of

As we emphasized in a 2014 Mathematical Investor blog, individual investors are not very well equipped, and certainly not very effective, in managing their own investment savings. They chronically fail to save enough, and very often mismanage what they do save.

This is unfortunate, because fewer workers than in the past, particularly in the U.S., are covered by a “defined-benefit” retirement system (pension). Instead, a growing fraction of workers rely on 401(k) systems or the equivalent, where they optionally contribute to an investment fund that they have either partial or full discretion to manage. More than

Examining charts is a long-standing fixture of modern finance. For example, we have all seen “scary charts”, which often spread like viruses. One example is the following (first chart). But as Matthew O’Brien pointed out, the scary parallel pretty much disappears if one scales the two charts properly (second chart):

Technical analysis

Charting typically goes hand-in-hand with “technical analysis,” namely the usage of relatively unsophisticated analysis schemes, typically computed from high-level statistics on stock market data. The field has its own terminology, as exemplified for example by a recent report in the Concord Register: parabolic stop and reverse

We have learned that two of our MAFFIA group (Bailey and Lopez de Prado), together with Jonathan Borwein (posthumously) and Amir Salehipour, were awarded the “Silver Bullet” award (from the “Dash of Insight” group) for our article “Evaluation and ranking of market forecasters.”

See the entry for 5/20/2017 HERE.

(Only Bailey’s name was listed at the above URL, but all authors should be equally credited.)